Abstract

In the field of machine learning and data mining, feature interaction is a ubiquitous issue that cannot be ignored and has attracted more attention in recent years. In this paper, we proposed the Symmetrical Complementary Coefficient which can quantify feature interactions very well. Based on it, we improved the Sequential Forward Selection (SFS) algorithm and proposed a new feature subset searching algorithm called SCom-SFS which only needs to consider the feature interactions between adjacent features on a given sequence instead of all of them. Moreover, discovered feature interactions can speed up the process of searching for the optimal feature subset. In addition, we have improved the ReliefF algorithm by screening out representative samples from the original data set, and need not to sample the samples. The improved ReliefF algorithm has been proved to be more efficient and reliable. An effective and complete feature selection algorithm RRSS is obtained through the combination of the two modified algorithms. According to the experimental results, the proposed algorithm RRSS outperformed five classic and two latest feature selection algorithms in terms of size of resulting feature subset, Accuracy, Kappa coefficient, and adjusted Mean-Square Error (MSE).

中文翻译：

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具有对称互补系数的特征选择用于量化特征相互作用

摘要

在机器学习和数据挖掘领域，特征交互是一个不容忽视的普遍问题，近年来已引起越来越多的关注。在本文中，我们提出了对称互补系数，它可以很好地量化特征相互作用。在此基础上，我们改进了顺序前向选择（SFS）算法，并提出了一种称为SCom-SFS的新特征子集搜索算法，该算法只需要考虑给定序列上相邻特征之间的特征相互作用即可，而不必考虑所有特征。此外，发现的特征交互可以加快搜索最佳特征子集的过程。此外，我们通过从原始数据集中筛选出代表性样本来改进ReliefF算法，而无需对样本进行抽样。事实证明，改进的ReliefF算法更有效，更可靠。通过两种改进算法的组合，获得了有效而完整的特征选择算法RRSS。根据实验结果，所提出的算法RRSS在结果特征子集的大小，准确性，Kappa系数和调整后的均方误差（MSE）方面优于五个经典和两个最新的特征选择算法。